Albin’s Portfolio Analysis

(as of 8th August, 2025)

import sys
from pathlib import Path

sys.path.append(str(Path.cwd().parent))
import config
from src import data_ingestion
from src.symbols import Symbols
from src.portfolio import Portfolio
from src.benchmark import Benchmark

import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

0.1 Transaction Data

log_files = [
    config.TRANS_LOG_DIR / "albin_trans_log.csv",
]

master_log = data_ingestion.create_master_log(log_files).set_index("Date")
display(master_log.head())

	Type	Symbol	Quantity	Price	Amount	Commission	Currency	Description	Exchange	Source
Date
2024-03-11	buy	0P0000GB48.BO	26.17	95.53	-2499.88	0.0	INR	SIP	BSE	Mutual Fund Account
2024-03-11	Net Deposit	NaN	NaN	NaN	2499.88	NaN	INR	Deposit for SIP	India Market	Mutual Fund Account
2024-03-18	Net Deposit	NaN	NaN	NaN	2499.88	NaN	INR	Deposit for SIP	India Market	Mutual Fund Account
2024-03-18	buy	0P0001QYX3.BO	160.04	15.62	-2499.88	0.0	INR	SIP	BSE	Mutual Fund Account
2024-04-10	buy	0P0000GB48.BO	25.65	97.47	-2499.88	0.0	INR	SIP	BSE	Mutual Fund Account

1 Symbols

List of all identified symbols in the transaction log:

symbol_manager = Symbols(master_log)

symbol_manager.assess()

print("✅ The following symbols were automatically identified:")
found_df = symbol_manager.get_found()

symbol_df = symbol_manager.get_unified_df()
display(found_df[['Name','Type', 'Industry', 'Sector', 'Country', 'Currency']])

✅ The following symbols were automatically identified:

	Name	Type	Industry	Sector	Country	Currency
Symbol
0P0000GB48.BO	ICICI Prudential Large Cap Gr	mutualfund	None	None	None	INR
0P0001QYX3.BO	HDFC Defence Reg IDCW-R	mutualfund	None	None	None	INR
0P00005WE5.BO	Nippon India Value Fund	mutualfund	None	None	None	INR
0P0001QR5R.BO	ICICI Prudential Innovation Fund	mutualfund	None	None	None	INR
0P0000TFYS.BO	Nippon India Gold Savings Fund	mutualfund	None	None	None	INR
AVANTIFEED.NS	Avanti Feeds Limited	equity	Packaged Foods	Consumer Defensive	India	INR
TATASTEEL.NS	Tata Steel Limited	equity	Steel	Basic Materials	India	INR
FEDERALBNK.NS	The Federal Bank Limited	equity	Banks - Regional	Financial Services	India	INR
JYOTHYLAB.NS	Jyothy Labs Limited	equity	Household & Personal Products	Consumer Defensive	India	INR
IRCTC.NS	Indian Railway Catering & Tourism Corporation ...	equity	Railroads	Industrials	India	INR
0P0001784C.BO	Tata Digital India Reg Gr	mutualfund	None	None	None	INR
IDEA.NS	Vodafone Idea Limited	equity	Telecom Services	Communication Services	India	INR
JIOFIN.BO	Jio Financial Services Limited	equity	Asset Management	Financial Services	India	INR
ONGC.BO	Oil and Natural Gas Corporation Limited	equity	Oil & Gas Integrated	Energy	India	INR
GOLDIETF.NS	None	equity	None	None	None	INR
GATECH.NS	GACM Technologies Limited	equity	Asset Management	Financial Services	India	INR
JPPOWER.NS	Jaiprakash Power Ventures Limited	equity	Utilities - Independent Power Producers	Utilities	India	INR
ITC.NS	ITC Limited	equity	Tobacco	Consumer Defensive	India	INR

2 Portfolio Analysis

start_date, end_date, date_range, last_market_day = config.project_dates(
    master_log.index
)

portfolio = Portfolio(master_log, symbol_manager, date_range, last_market_day)
portfolio.calculate_holdings_and_value()
portfolio_value = portfolio.get_total_value_history()

2.1 Current Holdings

portfolio.get_current_holdings().round(2).sort_values(
    "Market Value (INR)", ascending=False
)

	Shares	Market Value (INR)
0P0000GB48.BO	367.99	40132.99
0P0000TFYS.BO	700.58	27147.97
0P0001784C.BO	104.34	4736.64
TATASTEEL.NS	28.00	4470.76
JIOFIN.BO	10.00	3253.00
IRCTC.NS	4.00	2908.60
GOLDIETF.NS	27.00	2343.60
ONGC.BO	10.00	2337.50
ITC.NS	5.00	2068.00
FEDERALBNK.NS	8.00	1589.04
JPPOWER.NS	6.00	113.64
IDEA.NS	8.00	53.76
GATECH.NS	100.00	51.00

2.2 Realized/Unrealized Returns Summary

portfolio.get_return_summary().round(2)

	Income	Realized Gains	Unrealized Gains	Total Return
0P0000TFYS.BO	0.0	0.00	3019.54	3019.54
0P0000GB48.BO	0.0	0.00	2134.81	2134.81
TATASTEEL.NS	0.0	0.00	453.05	453.05
JIOFIN.BO	0.0	0.00	239.87	239.87
JYOTHYLAB.NS	0.0	180.96	0.00	180.96
FEDERALBNK.NS	0.0	0.00	168.76	168.76
IRCTC.NS	0.0	0.00	92.86	92.86
GOLDIETF.NS	0.0	0.00	35.90	35.90
ITC.NS	0.0	0.00	2.74	2.74
IDEA.NS	0.0	0.00	-0.60	-0.60
JPPOWER.NS	0.0	0.00	-0.82	-0.82
GATECH.NS	0.0	0.00	-16.25	-16.25
ONGC.BO	0.0	0.00	-134.94	-134.94
0P0001784C.BO	0.0	0.00	-263.12	-263.12
AVANTIFEED.NS	0.0	-649.99	0.00	-649.99
0P00005WE5.BO	0.0	-868.66	0.00	-868.66
0P0001QR5R.BO	0.0	-1320.27	0.00	-1320.27
0P0001QYX3.BO	0.0	-2309.41	0.00	-2309.41

total_returns = (
    portfolio.holdings["unrealized_gains"]
    + portfolio.holdings["realized_gains"]
    + portfolio.holdings["income"]
)

2.3 Return Contribution per Holding

fig_holdings = go.Figure()

for symbol in total_returns.columns:
    fig_holdings.add_trace(
        go.Scatter(
            x=total_returns.index,
            y=total_returns[symbol],
            mode="lines",
            name=symbol,
            line=dict(width=1),
        )
    )

fig_holdings.update_layout(
    title_text="<b>Individual Holding Performance Over Time</b>",
    template="plotly_white",
    xaxis_title="Date",
    yaxis_title=f"Total Returns ({config.BASE_CURRENCY})",
)

fig_holdings.show()

3 Portfolio Vs. Benchmark Performance

benchmark_simulation = Benchmark(master_log, date_range, last_market_day)
benchmark_simulation.run_simulation()
benchmark_results = benchmark_simulation.get_results()

3.1 Portfolio Vs. Benchmark Market Value & Net Deposits

monthly_deposits = benchmark_results["NetDeposit"].resample("ME").sum()

fig = make_subplots(specs=[[{"secondary_y": True}]])

# Personal Portfolio Value
fig.add_trace(
    go.Scatter(
        x=portfolio_value.index,
        y=portfolio_value,
        mode="lines",
        name="Personal Portfolio",
        line=dict(color="green", width=2),
    ),
    secondary_y=False,
)

# Benchmark Value
fig.add_trace(
    go.Scatter(
        x=benchmark_results.index,
        y=benchmark_results["TotalValue"],
        mode="lines",
        name=f"{config.BENCHMARK_INDEX} Benchmark",
        line=dict(color="red", width=2),
    ),
    secondary_y=False,
)

# Cumulative Net Deposits
fig.add_trace(
    go.Scatter(
        x=benchmark_results.index,
        y=benchmark_results["NetDeposit"].cumsum(),
        mode="lines",
        name="Cumulative Net Deposits",
        line=dict(color="darkgrey", width=1, dash="dash"),
    ),
    secondary_y=False,
)

# Monthly Deposits/Withdrawals (on secondary y-axis)
fig.add_trace(
    go.Bar(
        x=monthly_deposits.index,
        y=monthly_deposits,
        name="Deposits / Withdrawals",
        marker_color="royalblue",
        opacity=0.4,
    ),
    secondary_y=True,
)

fig.update_layout(
    title_text=f"Portfolio Performance vs. {config.BENCHMARK_INDEX} Benchmark",
    template="plotly_white",
    barmode="relative",
    legend=dict(orientation="h"),
    xaxis=dict(
        title="Date",
        type="date"
    ),
)

fig.update_yaxes(
    title_text=f"<b>Portfolio Value ({config.BASE_CURRENCY})</b>", secondary_y=False
)
fig.update_yaxes(
    title_text="<b>Monthly Cash Flow</b>",
    secondary_y=True,
    showgrid=False,
    layer="below traces",
)

fig.show()

3.2 Portfolio Vs. Benchmark Monthly Income

portfolio_income = portfolio.get_monthly_income()
benchmark_income = benchmark_simulation.get_monthly_income()

fig_income_comp = go.Figure()

fig_income_comp.add_trace(
    go.Bar(
        x=portfolio_income.index,
        y=portfolio_income,
        name="Portfolio Income",
        marker_color="mediumseagreen",
    )
)

fig_income_comp.add_trace(
    go.Bar(
        x=benchmark_income.index,
        y=benchmark_income,
        name=f"{config.BENCHMARK_INDEX} Benchmark Income",
        marker_color="grey",
    )
)

fig_income_comp.update_layout(
    title_text="<b>Monthly Income Comparison</b>",
    template="plotly_white",
    barmode="group",
    xaxis_title="Date",
    yaxis_title=f"Net Income ({config.BASE_CURRENCY})",
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
)

fig_income_comp.show()